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        "author": "Paul Sinclair",
        "author_email": "pbsinclair42@gmail.com",
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            "Development Status :: 4 - Beta",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# MCTS\n\nThis package provides a simple way of using Monte Carlo Tree Search in any perfect information domain.  \n\n## Installation \n\nWith pip: `pip install mcts`\n\nWithout pip: Download the zip/tar.gz file of the [latest release](https://github.com/pbsinclair42/MCTS/releases), extract it, and run `python setup.py install`\n\n## Quick Usage\n\nIn order to run MCTS, you must implement a `State` class which can fully describe the state of the world.  It must also implement four methods: \n\n- `getPossibleActions()`: Returns an iterable of all actions which can be taken from this state\n- `takeAction(action)`: Returns the state which results from taking action `action`\n- `isTerminal()`: Returns whether this state is a terminal state\n- `getReward()`: Returns the reward for this state.  Only needed for terminal states. \n\nYou must also choose a hashable representation for an action as used in `getPossibleActions` and `takeAction`.  Typically this would be a class with a custom `__hash__` method, but it could also simply be a tuple or a string.  \n\nOnce these have been implemented, running MCTS is as simple as initializing your starting state, then running:\n\n```python\nfrom mcts import mcts\n\nmcts = mcts(timeLimit=1000)\nbestAction = mcts.search(initialState=initialState)\n```\nSee [naughtsandcrosses.py](https://github.com/pbsinclair42/MCTS/blob/master/naughtsandcrosses.py) for a simple example.  \n\n## Slow Usage\n//TODO\n\n## Collaborating\n\nFeel free to raise a new issue for any new feature or bug you've spotted. Pull requests are also welcomed if you're interested in directly improving the project.\n\n\n",
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        "license": "MIT",
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        "name": "mcts",
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